Research shows that recommendations comprise a valuable service for users of a digital library. We proposed a hybrid document recommender system based on random walk. It builds correlation network among users based on the conditional probability in order to solve the sparsity of collaborative filtering. On the other hand, it computes the rating of source user for target item not only based on the neighborhoods’ ratings for target item but also based on the neighborhoods’ ratings for item which is most similar to target item. This can solve the cold start problem of recommender systems. We performed an evaluation on the dataset of National Science and Technology Library. Experimental results illustrate the superiority of the proposed method.
Data mining discovers knowledge and useful information from large amounts of data stored in databases. With the increasing popularity of object-oriented database system in advanced database applications, it is significantly important to study the data mining methods for object-oriented database. This paper proposes that higher-order logic programming languages and techniques is very suitable for object-oriented data mining, and presents a framework for object-oriented data mining based on higher-order logic programming. Such a framework is inductive logic programming which adopts higher-order logic programming language Escher as knowledge representation formalism. In addition, Escher is a generalization of the attribute-value representation, thus many higher-order logic learners under this framework can be upgraded directly from corresponding propositional learners.
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